Evaluating and Understanding Model Editing for Medical Vision Language Models
Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining. However, existing multimodal model editing benchmarks focus on general-purpose tasks and do not reflect realistic clinical domain requirements and variability. To address this, we introduce M3Bench, a clinically grounded benchmark for multimodal model editing that evaluates whether an edit remains reliable, precise, and generalizable under the challenges of image and text variation, modality and protocol shifts, clinical knowledge composition, and
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 54%Atomic-man007/Awesome_Multimodel_LLM →
- PossiblePossibly related (embedding) · 53%vlm-starter →
- PossiblePossibly related (embedding) · 50%VioletVision-3B →
- PossiblePossibly related (embedding) · 50%apanariello4/merge-and-rebase →
- PossiblePossibly related (embedding) · 49%sgl-project/sglang →
- LinkedLinked via arxiv author · 85%Guli Zhu →
“Evaluating and Understanding Model Editing for Medical Vision Language Models”
- LinkedLinked via arxiv author · 85%Chenwei Wu →
“Evaluating and Understanding Model Editing for Medical Vision Language Models”
- LinkedLinked via arxiv author · 85%Liyue Shen →
“Evaluating and Understanding Model Editing for Medical Vision Language Models”